If you get surprised by a change from a low-bias regime to a high-bias one, your old assumptions will lead you to be overconfident in your model. For 538, this just meant publishing numbers that were way too confident. But in the financial context, overconfident modeling means you think you can use more leverage than you actually can.
This isn't the case for the 2016 elections. 538 ended up having a much more robust forecast because they accounted for systematic polling error (bias).
https://fivethirtyeight.com/features/why-fivethirtyeight-gav...
538 was just about the only polling aggregrator that WASN'T guilty of this during 2016 and were relatively bullish on Trump.
Their mistake during 2016 was releasing a series of editorials not really based on data at all that predicted Trump would eventually peak in support and the remaining voters would rally behind a sensible seeming conservative establishment choice like Marco Rubio. Their theory was plausible and completely wrong. Yet their numbers consistently predicted Trump victory in the primsries as he was an eternal frontrunner for virtually the entire primary.
Whenever historical growth rates have always been positive, markets react in shock at the "black swan" event when a 30, 50 or 100 year pattern is all of a sudden broken.
Unfortunately, the balloon payments made this impossible for most.
Specifically, the models used assumed that the economic risk of a mortgage default in Connecticut was uncorrelated with the economic risk of a mortgage default in Texas. When housing prices are rising nationally, this assumption is basically true - the house can be sold by the bank for what's owed on the mortgage, refinancing is available for balloon payments, and any one borrower's cash flow situation is idiosyncratic. But when housing prices started to fall, this lack of correlation broke down, and many financial institutions became unexpectedly responsible for the losses in this asset class.